Senior Site Reliability Engineer - AI Research Clusters

About the position

NVIDIA is the leader in AI, machine learning and datacenter acceleration. NVIDIA is expanding that leadership into datacenter networking with ethernet switches, NICs and DPUs. NVIDIA has continuously reinvented itself over two decades. Our invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI - the next era of computing. NVIDIA is a 'learning machine' that constantly evolves by adapting to new opportunities that are hard to solve, that only we can tackle, and that matter to the world. This is our life's work, to amplify human imagination and intelligence. Make the choice, join our diverse team today! As a member of the GPU AI/HPC Infrastructure team, you will provide leadership in the design and implementation of ground breaking GPU compute clusters that powers all AI research across NVIDIA. We seek an expert to build and operate these clusters at high reliability, efficiency, and performance and drive foundational improvements and automation to improve researchers productivity. As a Site Reliability Engineer, you are responsible for the big picture of how our systems relate to each other, we use a breadth of tools and approaches to tackle a broad spectrum of problems. Practices such as limiting time spent on reactive operational work, blameless postmortems and proactive identification of potential outages factor into iterative improvement that is key to both product quality and interesting dynamic day-to-day work. SRE's culture of diversity, intellectual curiosity, problem solving and openness is important to our success. Our organization brings together people with a wide variety of backgrounds, experiences and perspectives. We encourage them to collaborate, think big and take risks in a blame-free environment. We promote self-direction to work on meaningful projects, while we also strive to build an environment that provides the support and mentorship needed to learn and grow.

Responsibilities

• Design and implement state-of-the-art GPU compute clusters.
• Optimize cluster operations for maximum reliability, efficiency, and performance.
• Drive foundational improvements and automation to enhance researcher productivity.
• Tackle strategic challenges in large-scale, high-performance computing environments.
• Troubleshoot, diagnose and root cause of system failures and isolate the components/failure scenarios while working with internal & external partners.
• Scale systems sustainably through mechanisms like automation, and evolve systems by pushing for changes that improve reliability and velocity.
• Practice sustainable incident response and blameless postmortems.
• Be part of an on call rotation to support production systems.
• Write and review code, develop documentation and capacity plans, debug the hardest problems, live, on some of the largest and most complex systems in the world.
• Implement remediations across software and hardware stack according to plan, while keeping a thorough procedural record and data log.
• Manage upgrades and automated rollbacks across all clusters.

Requirements

• Bachelor's degree in Computer Science, Electrical Engineering or related field or equivalent experience with a minimum 6+ years of experience designing and operating large scale compute infrastructure.
• Proven experience in site reliability engineering for high-performance computing environments with operational experience of at least 5K GPUs cluster.
• Deep understanding of GPU computing and AI infrastructure.
• Passion for solving complex technical challenges and optimizing system performance.
• Experience with AI/HPC advanced job schedulers, and ideally familiarity with schedulers such as Slurm.
• Solid experience with GPU clusters, and working knowledge of cluster configuration management tools such as BCM or Ansible and infrastructure level applications, such as Kubernetes, Terraform, MySQL, etc.
• In depth understanding of container technologies like Docker, Enroot, etc.
• Experience programming in Python and Bash scripting.

Nice-to-haves

• Interest in crafting, analyzing and fixing large-scale distributed systems.
• Familiarity with NVIDIA GPUs, Cuda Programming, NCCL and MLPerf benchmarking.
• Familiarity with InfiniBand with IBoIP and RDMA.
• Experience with Cloud Deployment, BCM, Terraform.
• Understanding of fast, distributed storage systems like Lustre and GPFS for AI/HPC workloads.
• Familiarity with deep learning frameworks like PyTorch and TensorFlow.
• Multi-cloud experience.

Benefits

• Highly competitive salaries
• Comprehensive benefits package
• Equity opportunities
• Ongoing applications accepted